Λεπτομέρειες βιβλιογραφικής εγγραφής
| Τίτλος: |
Android-Based Real-Time Classification of Electric Fire Short-Circuit Traces Using Lightweight Deep Learning Model. |
| Συγγραφείς: |
Nazari, Mohammad Hadi, Bang, Junho |
| Πηγή: |
Energies (19961073); Jul2026, Vol. 19 Issue 13, p3184, 21p |
| Θεματικοί όροι: |
Convolutional neural networks, Fault diagnosis, Deep learning, Electric faults, Classification algorithms |
| Reviews & Products: |
Android (Operating system) |
| Περίληψη: |
This paper presents a lightweight deep learning framework for classifying electric fire short-circuit traces to enhance safety and fault diagnosis in electrical energy systems. Accurate differentiation between primary (PSCT) and secondary short-circuit traces (SSCT) is essential for identifying failure origins, yet conventional manual inspection is time-consuming and subjective. To address these limitations, we systematically evaluate three lightweight convolutional neural network (CNN) architectures MobileNetV2, MobileNetV3, and EfficientNet using transfer learning on a domain-specific image dataset. The models are assessed based on accuracy, loss, precision, recall, and F1-score. Experimental results show that EfficientNet achieves the highest classification accuracy, while MobileNetV3 demonstrates the lowest validation loss and superior generalization stability. Based on a performance–efficiency trade-off analysis, MobileNetV3 is deployed on an Android platform using TensorFlow Lite, enabling real-time, offline, and on-device inference. To the best of our knowledge, this is among the first studies to integrate lightweight CNN-based short-circuit trace classification with real-time mobile deployment for on-site energy system fault analysis. By bridging the gap between deep learning and field deployment, the proposed mobile system ensures low-latency execution and provides a rapid, reliable, and portable solution for improving operational safety in electrical fire investigations. [ABSTRACT FROM AUTHOR] |
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| Βάση Δεδομένων: |
Complementary Index |